We are developing a multiple discriminant logistic regression model for predicting the occurrence of a myocardial infarction (MI) in the context of patient admissions and discharges from any emergency room. The model is based on empirical observations of 174 variables available in the Emergency Room (ER), including patients' risk factors, presenting historical and clinical parameters, initial ECGs, symptomatology, and social/organizational factors on admission/discharge decisions. The 174 variables are being reduced by cluster analysis into mutually exclusive and clinically logical clusters which will be correlated with the final diagnosis of MI, false positive admissions, and false negative discharges. From these correlations, equations predicting each of these outcomes will be developed. Lastly, over a period of a year, during alternate months, the predictive results of these equations will be available to the admitting ER physicians on a patient-by-patient basis. The utility of such predictive equations as a supplement to the clinical decision making process in the ER will be examined as to their effect on maximizing appropriate admissions and reducing false positive admissions and false negative discharges. This model, if demonstrated efficacious, can easily be extrapolated to admission decisions regarding a large array of other common medical problems seen in Emergency Rooms.